In our model, agents do not search randomly. Instead, they follow public popularity information generated by choices made by previous searchers in the market. After the first agent, a second or \(m^{th}\)  agent is asked to make its choice. Now the agent can observe the sum of choices made by previous searchers in the form of a popularity vector \(P= \lbrace P_1,...,P_N\rbrace \)  that records the choices of alternatives \(X_1,...,X_N\)  and is updated whenever an individual makes a choice. Examples of such popularity information would be product sales, number of song downloads, or number of article citations. The agents sample the alternatives \( X_1,X_2...X_n \)  in decreasing order of popularity \(P_1 \ge P_2 \ge ... \ge P_n\) , where popularity is defined as the number of times that an alternative has been selected in the past. When there is a tie, the agents choose which one to sample next at random. We call the order in which the alternatives are searched the search path. The agents stop search when they encounter an alternative \(u_n\)  with utility higher than a threshold \(T\) ; otherwise, they continue sampling until the alternatives are exhausted. The proposed model captures the behavioral regularities of search in online interfaces [1]}, [2]} and bolsters them with a fully fleshed-out utility framework.
